{"title":"利用机器学习增强流感和COVID-19的诊断","authors":"Dang Nhu Phu, Phan Cong Vinh, Nguyen Kim Quoc","doi":"10.4108/eetcasa.v9i1.4030","DOIUrl":null,"url":null,"abstract":"The Coronavirus Disease 2019 (COVID-19) has rapidly spread globally, causing a significant impact on public health. This study proposes a predictive model employing machine learning techniques to distinguish between influenza-like illness and COVID-19 based on clinical symptoms and diagnostic parameters. Leveraging a dataset sourced from BMC Med Inform Decis Mak, comprising cases of influenza and COVID-19, we explore a diverse set of features, including clinical symptoms and blood assay parameters. Two prominent machine learning algorithms, XGBoost and Random Forest, are employed and compared for their predictive capabilities. The XGBoost model, in particular, demonstrates superior accuracy with an AUC under the ROC curve of 98.8%, showcasing its potential for clinical diagnosis, especially in settings with limited specialized testing equipment. Our model's practical applicability in community-based testing positions it as a valuable tool for efficient COVID-19 detection. This study advances the field of predictive modeling for disease detection, offering promising prospects for improved public health outcomes and pandemic response strategies. The model's reliability and effectiveness make it a valuable asset in the ongoing fight against the COVID-19 pandemic.","PeriodicalId":500308,"journal":{"name":"EAI endorsed transactions on context-aware systems and applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning\",\"authors\":\"Dang Nhu Phu, Phan Cong Vinh, Nguyen Kim Quoc\",\"doi\":\"10.4108/eetcasa.v9i1.4030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Coronavirus Disease 2019 (COVID-19) has rapidly spread globally, causing a significant impact on public health. This study proposes a predictive model employing machine learning techniques to distinguish between influenza-like illness and COVID-19 based on clinical symptoms and diagnostic parameters. Leveraging a dataset sourced from BMC Med Inform Decis Mak, comprising cases of influenza and COVID-19, we explore a diverse set of features, including clinical symptoms and blood assay parameters. Two prominent machine learning algorithms, XGBoost and Random Forest, are employed and compared for their predictive capabilities. The XGBoost model, in particular, demonstrates superior accuracy with an AUC under the ROC curve of 98.8%, showcasing its potential for clinical diagnosis, especially in settings with limited specialized testing equipment. Our model's practical applicability in community-based testing positions it as a valuable tool for efficient COVID-19 detection. This study advances the field of predictive modeling for disease detection, offering promising prospects for improved public health outcomes and pandemic response strategies. The model's reliability and effectiveness make it a valuable asset in the ongoing fight against the COVID-19 pandemic.\",\"PeriodicalId\":500308,\"journal\":{\"name\":\"EAI endorsed transactions on context-aware systems and applications\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EAI endorsed transactions on context-aware systems and applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4108/eetcasa.v9i1.4030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI endorsed transactions on context-aware systems and applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eetcasa.v9i1.4030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
2019冠状病毒病(COVID-19)在全球迅速蔓延,对公共卫生造成重大影响。本研究提出了一种基于临床症状和诊断参数,利用机器学习技术区分流感样疾病和COVID-19的预测模型。利用来自BMC Med Inform Decis Mak的数据集(包括流感和COVID-19病例),我们探索了一系列不同的特征,包括临床症状和血液检测参数。两种著名的机器学习算法,XGBoost和Random Forest,被用来比较它们的预测能力。特别是XGBoost模型,在ROC曲线下的AUC为98.8%,显示了其在临床诊断中的潜力,特别是在专业测试设备有限的情况下。我们的模型在社区检测中的实际适用性使其成为有效检测COVID-19的宝贵工具。该研究推动了疾病检测预测建模领域的发展,为改善公共卫生结果和大流行应对策略提供了良好的前景。该模型的可靠性和有效性使其成为当前抗击COVID-19大流行的宝贵资产。
Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning
The Coronavirus Disease 2019 (COVID-19) has rapidly spread globally, causing a significant impact on public health. This study proposes a predictive model employing machine learning techniques to distinguish between influenza-like illness and COVID-19 based on clinical symptoms and diagnostic parameters. Leveraging a dataset sourced from BMC Med Inform Decis Mak, comprising cases of influenza and COVID-19, we explore a diverse set of features, including clinical symptoms and blood assay parameters. Two prominent machine learning algorithms, XGBoost and Random Forest, are employed and compared for their predictive capabilities. The XGBoost model, in particular, demonstrates superior accuracy with an AUC under the ROC curve of 98.8%, showcasing its potential for clinical diagnosis, especially in settings with limited specialized testing equipment. Our model's practical applicability in community-based testing positions it as a valuable tool for efficient COVID-19 detection. This study advances the field of predictive modeling for disease detection, offering promising prospects for improved public health outcomes and pandemic response strategies. The model's reliability and effectiveness make it a valuable asset in the ongoing fight against the COVID-19 pandemic.